• Automating outreach to customers in sales fullest.
  • Increase customer-facing time, increase customer satisfaction, make efficiency improvements …
Sales strategy and planning Forecasting, channel strategy, resource allocation, talent management
Lead identification & qualification Pipeline management, action plans for new & existing customers
Configuration, pricing, and quoting (CPQ) Quota setting, configuration of technical solution, negotiating, contracting
Order management Credit checking, invoicing, order-related service handling
Postsales activities Regular follow-ups, handling of incoming requests (eg, spare parts, repairs, etc.)
Structural support Reporting, analytics, training, provision of sales support materials, administrative tasks
  • Bid process: proposal time decrease; manual – assembling docs., looking up specifications, putting together the proposal
  • Decrease order processing times – confirmed order until delivery
  • License certificates
  • Standardize sales processes
  • Sales support
  • CRM: lean, simple, digitized; workshops, create buy-in, and hedge against risks, prioritized use cases
  • Competitive landscape
  • Customer preferences
  • KPIs: revenue growth, acquisition rate
  • Faster turnaround
  • Priority customers
  • Unforeseen delays, talking points, profit margins
  • Sales performance: opportunity identification, negotiation preparation, customer interaction

Use cases exist all along the sales value chain.

Examples include:

—Lead management. Chatbots enable companies to re-engage prospective customers who are stuck in the purchasing funnel, thus creating new opportunities without any extra human effort. The bot independently selects customers, contacts them through text message or email, uses natural-language processing to understand the context of their response, and answers accordingly to drive conversion. This solution can increase sales reps’ selling time by 15 to 20 percent, while increasing deal-flow transparency and conversion.

—Churn prevention. Scoring tools can create 360-degree customer profiles automatically, leveraging variables such as buying patterns, interaction preferences, and web data to identify customers with the highest propensity to churn. Compared to previous models based on simple analytics, machine learning triples the predictive power to identify churners. Based on the ML output, sales and marketing staff can take targeted actions to prevent churn, such as preconfigured price discounts to incentivize customers.

—RFP generation. Solutions based on natural-language processing/generation and robotic process automation can help reduce the time it takes to draft requests for proposals (RFPs) by up to two-thirds and eliminate human error. For example, one solution decodes the questions to be answered and proposes responses in a customized file that can be automatically sent to the prospective customer. This kind of solution has the potential to speed up RFP response time and efficiency while also improving internal version tracking and storage of relevant RFP content.

 —Post-sales customer journey optimization. Robotic process automation and virtual agents can be used to reinvent the customer journey and create a seamless online process for ordering, tracking, and query management. For example, this approach helped a B2B supplier increase its customer-satisfaction score by 24 percentage points and improve throughput by about 20 percent.

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